Conventional techniques of video surveillance systems, which involve face detection recognition, use a face detection algorithm that works continuously for detecting a face in an image. The face detection algorithm works on all images present in a video recorded by an image sensor. The image sensor may be a camera, an infrared sensor, or a thermal sensor, or any other device known in the art for capturing images. Continuous monitoring of the images for face detection consumes a lot of memory and processing time. Further, the face detection algorithm may falsely detect a face from a background of the image. This may lead to non-identification of an intended user or false detection of an unintended subject. The face detection algorithm may be used for processing video stream captured by a video surveillance system. Further, the face detection algorithm may be used for detecting faces of different persons present in the video stream.
For an example, an Automatic Telling Machine (ATM) widely makes use of the face detection algorithm for detecting a user accessing the ATM. The user accessing the ATM may cover a part of his face while attempting a deceitful action. For an example, the user may cover his mouth for making a false detection by the ATM. During such a situation, the face detection algorithm may falsely detect the user based on eyes of the user.